In just three years, e-scooters have substantially disrupted and altered the urban mobility landscape. Throughout this period, they have been commonly touted as part of a larger micromobility solution that promises to erase equity barriers and solve the firstmile/last-mile problem. However, few studies in the nascent e-scooter literature have considered these claims. In this study, we surveyed students at Portland State University (n = 1,968) about the role that e-scooters, among other modes, played in meeting their general and university-related travel needs. We then estimated models that incorporated demographics, travel behavior, and latent attitudes distilled using exploratory factor analysis (EFA). These models were used to assess the current performance of e-scooters in meeting equity and mode-shift goals. We first estimated ordinal logit models to understand the relationship of these factors to the stated number of trips taken in the 7 days prior to the survey by escooter, car, bike, and MAX light rail. Perceived propensity to switch to using e-scooter, car, bike, or MAX light rail modes for commuting to the university should their present primary commute mode became unavailable. We also designed and implemented a stated choice experiment (SCE) consisting of several hypothetical scenarios of a commute to PSU. In the SCE, students were given a three-mode labelled set consisting of car, bike, and e-scooter + MAX choices. The experiment choice sets were designed using a D-Efficient method. In order to understand the relationship of travel time and First and foremost, I want to recognize the loving support of Ian McQueen throughout this and countless other challenging journeys. I am immensely grateful for the unending grace and patience he has shown while my energies were focused on this endeavor, and I am looking forward to being more present in our next chapter. I want to thank my advisor, Dr. Kelly J. Clifton, for originally convincing me to move to Portland and for proving to me that I worry too much. Her expertise, non-anxious presence, and self-described bad jokes were just what I needed to accomplish this undertaking. I also want to thank John MacArthur for giving me countless opportunities to learn and apply an incredible number of new skills throughout our research together. Additionally, I am grateful to him for letting me borrow his hot-rod e-bike-for research purposes, of course. Thanks to Dr. Christopher Monsere and Dr. Liming Wang for agreeing to participate on my committee. Your comments and input have been extremely valuable. I appreciate the help of Dr. Kristina Currans in the early stages of defining this project, despite the fact that I was not even a student at her university. I am grateful for the tutelage of Dr. Jason Newsom, who single-handedly managed to teach me a solid foundational understanding of statistics, skills which I hope were put to good use in this research. Thanks to my classmates, colleagues, and concurrent friends Gabby Abou
